Hints Help Finding and Fixing Bugs Differently in Python and Text-based Program Representations
Ruchit Rawal, Victor-Alexandru P\u{a}durean, Sven Apel, Adish Singla,, Mariya Toneva

TL;DR
This study investigates how different types of hints and program representations affect bug fixing in AI-assisted programming, revealing that natural text representations and personalized hints significantly improve user accuracy.
Contribution
It provides empirical evidence on the impact of hint types and program representations on bug fixing, highlighting the importance of personalized support in programming tools.
Findings
Users are more accurate with text-based program representations.
Hints improve bug fixing accuracy, with effectiveness varying by hint type.
Program representation significantly influences bug fixing success.
Abstract
With the recent advances in AI programming assistants such as GitHub Copilot, programming is not limited to classical programming languages anymore--programming tasks can also be expressed and solved by end-users in natural text. Despite the availability of this new programming modality, users still face difficulties with algorithmic understanding and program debugging. One promising approach to support end-users is to provide hints to help them find and fix bugs while forming and improving their programming capabilities. While it is plausible that hints can help, it is unclear which type of hint is helpful and how this depends on program representations (classic source code or a textual representation) and the user's capability of understanding the algorithmic task. To understand the role of hints in this space, we conduct a large-scale crowd-sourced study involving 753 participants…
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Taxonomy
TopicsComputational Physics and Python Applications · Advanced Malware Detection Techniques
